Predicting intent of a search for a particular context

ABSTRACT

A computing system is described that determines, based on user-initiated actions performed by a group of computing devices, an intent of a search using a particular search query received from a computing device. The computing system adjusts, based on the intent, at least a particular portion of search results obtained from the search using the search query by emphasizing information that satisfies the intent. The computing system sends, to the computing device, an indication of the adjusted search results.

BACKGROUND

A user may turn to a computing device for obtaining information andfacts that might assist the user in accomplishing a certain task. Somecomputing devices require that the user be able to provide sufficientinformation (e.g., search query terms) for guiding the computing devicetowards locating the particular information that the user is searchingfor. If a search query is not narrowly tailored, or if the user does notprovide much in the way of additional information beyond the query, acomputing device may return too much information; with some of the mostinteresting or relevant information being difficult for a user to find.The user may experience stress and/or waste valuable time and resourcesinputting very detailed queries and into a computing device, causing thecomputing device to execute multiple searches, or sifting through largequantities of search results, to obtain information necessary toaccomplish the certain task.

SUMMARY

In general, techniques of this disclosure may enable a computing systemto predict an intent of a search query for a particular context of acomputing device. Based on contextual information (e.g., locations, userinterest, times of day, etc.) of a computing device/user, the system maydefine a relevant context for a search query, and predict, based on therelevant context, an intent or purpose of a search using the searchquery in the relevant context. The system may adjust, based on theintent, search results returned from the search so that information forsatisfying the intent is emphasized over other information returned fromthe search. For instance, after a user of a computing device purchasestickets to a particular movie that is out in movie theatres, a user maycause the computing system to execute a search using the name of theparticular movie as part of a query. The system may obtain contextualinformation including an indication that the tickets were alreadypurchased for a future showing of the particular movie. In response, thesystem may infer (e.g., based on log data indicative of user-initiatedactions performed by other computing devices) that the search includingthe name of the particular movie is for a purpose other than purchasingadditional tickets. The system may therefore adjust the search resultsreturned from the search so that movie show times are ranked lower thanother information (e.g., reviews, memorabilia, trivia, etc.) about theparticular movie. In this way, by automatically adjusting the searchresults to emphasize the information a user is more likely to besearching for, in a current context, the system may allow users toexperience less stress and/or not waste valuable time and resourceshunting for the information in and amongst search results.

Throughout the disclosure, examples are described where a computingdevice and/or a computing system analyzes information (e.g., context,locations, speeds, search queries, etc.) associated with a computingdevice and a user of a computing device, only if the computing devicereceives permission from the user of the computing device to analyze theinformation. For example, in situations discussed below, before acomputing device or computing system can collect or may make use ofinformation associated with a user, the user may be provided with anopportunity to provide input to control whether programs or features ofthe computing device and/or computing system can collect and make use ofuser information (e.g., information about a user's current location,current speed, etc.), or to dictate whether and/or how to the deviceand/or system may receive content that may be relevant to the user. Inaddition, certain data may be treated in one or more ways before it isstored or used by the computing device and/or computing system, so thatpersonally-identifiable information is removed. For example, a user'sidentity may be treated so that no personally identifiable informationcan be determined about the user, or a user's geographic location may begeneralized where location information is obtained (such as to a city,ZIP code, or state level), so that a particular location of a usercannot be determined. Thus, the user may have control over howinformation is collected about the user and used by the computing deviceand computing system.

In one example, the disclosure is directed to a method that includesdetermining, by a computing system, based on user-initiated actionsperformed by a group of computing devices and contextual information ofa computing device, an intent of a search using a particular searchquery received from the computing device, adjusting, based on theintent, at least a particular portion of search results obtained fromthe search using the search query by emphasizing information thatsatisfies the intent, and sending, by the computing system, to thecomputing device, an indication of the adjusted search results.

In another example, the disclosure is directed to a system that includesmeans for determining, based on user-initiated actions performed by agroup of computing devices and contextual information of a computingdevice, an intent of a search using a particular search query receivedfrom the computing device, means for adjusting, based on the intent, atleast a particular portion of search results obtained from the searchusing the search query by emphasizing information that satisfies theintent, and means for sending, to the computing device, an indication ofthe adjusted search results.

In another example, the disclosure is directed to a computing systemthat includes at least one processor and a memory that includesinstructions that, when executed, cause the at least one processor todetermine, based on user-initiated actions performed by a group ofcomputing devices and contextual information of a computing device, anintent of a search using a particular search query received from thecomputing device, adjust, based on the intent, at least a particularportion of search results obtained from the search using the searchquery by emphasizing information that satisfies the intent, and send, tothe computing device, an indication of the adjusted search results.

In another example, the disclosure is directed to a computer-readablestorage medium including instructions that, when executed, configure oneor more processors of a computing system to determine, based onuser-initiated actions performed by a group of computing devices andcontextual information of a computing device, an intent of a searchusing a particular search query received from the computing device,adjust, based on the intent, at least a particular portion of searchresults obtained from the search using the search query by emphasizinginformation that satisfies the intent, and send, to the computingdevice, an indication of the adjusted search results.

The details of one or more examples are set forth in the accompanyingdrawings and the description below. Other features, objects, andadvantages of the disclosure will be apparent from the description anddrawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a conceptual diagram illustrating an example system forpredicting an intent associated with a search query and adjusting searchresults based on the intent, in accordance with one or more aspects ofthe present disclosure.

FIG. 2 is a block diagram illustrating an example computing systemconfigured to predict an intent associated with a search query andadjust search results based on the intent, in accordance with one ormore aspects of the present disclosure.

FIG. 3 is a flowchart illustrating example operations performed by anexample computing system configured to predict an intent associated witha search query and adjust search results based on the intent, inaccordance with one or more aspects of the present disclosure.

DETAILED DESCRIPTION

FIG. 1 is a conceptual diagram illustrating an example system forpredicting an intent associated with a search query and adjusting searchresults based on the intent, in accordance with one or more aspects ofthe present disclosure. System 100 includes information server system(“ISS”) 160 in communication, via network 130, with computing devices110A-110N (collectively, “computing devices 110”).

Network 130 represents any public or private communications network, forinstance, cellular, Wi-Fi, and/or other types of networks, fortransmitting data between computing systems, servers, and computingdevices. ISS 160 may exchange data, via network 130, with computingdevices 110 to provide a prediction and service that is accessible tocomputing devices 110 when computing devices 110 are connected tonetwork 130.

Network 130 may include one or more network hubs, network switches,network routers, or any other network equipment, that are operativelyinter-coupled thereby providing for the exchange of information betweenISS 160 and computing devices 110. Computing devices 110 and ISS 160 maytransmit and receive data across network 130 using any suitablecommunication techniques. Computing devices 110 and ISS 160 may each beoperatively coupled to network 130 using respective network links. Thelinks coupling computing devices 110 and ISS 160 to network 130 may beEthernet or other types of network connections and such connections maybe wireless and/or wired connections.

ISS 160 hosts (or at least provides access to) a search service. In thecourse of providing search services, ISS 160 may communicate withcomputing device 110 to obtain information needed for completing a task.ISS 160 represents any suitable remote computing system, such as one ormore desktop computers, laptop computers, mainframes, servers, cloudcomputing systems, etc. capable of sending and receiving informationboth to and from a network, such as network 130. In some examples, ISS160 represents cloud computing systems that provides access to thesearch service via a cloud.

Computing devices 110 represent individual mobile or non-mobilecomputing devices that are configured to access the search serviceprovided via network 130. Computing devices 110 may communicate with ISS160 via network 130 to access the search service provided by ISS 160.Examples of computing devices 110 include a mobile phone, a tabletcomputer, a laptop computer, a desktop computer, a server, a mainframe,a set-top box, a television, a wearable device (e.g., a computerizedwatch, computerized eyewear, computerized gloves, etc.), a homeautomation device or system (e.g., an intelligent thermostat, a securitysystem, a table-top assistant device), a voice-interface or countertophome assistant device, a personal digital assistants (PDA), a gamingsystem, a media player, an e-book reader, a mobile television platform,an automobile navigation or infotainment system, or any other type ofmobile, non-mobile, wearable, and non-wearable computing deviceconfigured to access a search service and receive information via anetwork, such as network 130.

In the example of FIG. 1, ISS 160 includes context module 162,prediction module 164, and search module 166. Each of computing devices110 includes a respective user interface device 112A-112N (collectively“UICs 112”) and a respective user interface (UI) module 120A-120N(collectively “UI modules 120”). In addition, each of computing devices110 includes a respective query module 122A-122N (collectively “querymodules 122”).

Modules 162, 164, 166, 120, and 122 may perform operations describedusing software, hardware, firmware, or a mixture of hardware, software,and firmware residing in and/or executing at ISS 160 or computingdevices 110. ISS 160 and computing devices 110 may execute modules 162,164, 166, 120, and 122 with multiple processors or multiple devices. ISS160 and computing devices 110 may execute modules 162, 164, 166, 120,and 122 as virtual machines executing on underlying hardware. Modules162, 164, 166, 120, and 122 may execute as one or more services of anoperating system or computing platform. Modules 162, 164, 166, 120, and122 may execute as one or more executable programs at an applicationlayer of a computing platform.

Computing devices 110B-110N constitute a group of computing devices fromwhich respective users associated with computing devices 110B-110N mayperform searches for information. In some examples, computing device110A is included in the group with computing devices 110B-110N and auser associated with computing device 110A may also perform searches forinformation from computing device 110A. In other examples the group ofcomputing devices 110B-110N excludes computing device 110A.

UICs 112 of computing devices 110 may function as respective inputand/or output devices for computing devices 110. UICs 112 may beimplemented using various technologies. For instance, UICs 112 mayfunction as an input device using presence-sensitive input screens,microphone technologies, infrared sensor technologies, cameras, or otherinput device technology for use in receiving user input. UICs 112 mayfunction as output device configured to present output to a user usingany one or more display devices, speaker technologies, haptic feedbacktechnologies, or other output device technology for use in outputtinginformation to a user.

UI modules 120 may manage user interactions with UICs 112 and othercomponents of computing devices 110 and may interact with ISS 160 toprovide search services via UICs 112. UI modules 120 may causerespective UICs 112 to output respective user interfaces as respectiveuser of computing devices 110 views output and/or provides input at UICs112. For example, as shown in FIG. 1, UI module 120A may sendinstructions to UIC 112A that cause UIC 112A to display user interface114 at a display screen of UIC 112A.

In the example of FIG. 1, user interface 114 is a graphical userinterface associated with a search service provided by ISS 160 andaccessed by computing device 110A. As described in detail below, userinterface 114 includes graphical information (e.g., text), whichrepresents adjusted search results that ISS 160 returns from a searchusing a search query received from computing device 110A. In someexamples, User interface 114 may be an audible user interface, a hapticuser interface, or a combination graphical/audible/haptic userinterface. User interface 114 may present search results in variousforms, such as forms such as audible sounds, vibrations, text, graphics,content cards, images, or any other visual, audible, and/or haptic form.

UI modules 120 and UICs 112 may receive one or more indications of input(e.g., voice input, touch input, non-touch or presence-sensitive input,video input, audio input, etc.) from a user as the user interacts with auser interface, at different times and when users and computing devices110 are at different locations. UI modules 120 and UICs 112 mayinterpret inputs detected at UICs 112 and may relay information aboutthe inputs detected at UICs 112 to other modules of system 100,including modules 162, 164, 166, and 122.

UI modules 120 may receive information and instructions from one or moreassociated platforms, operating systems, applications, and/or servicesexecuting at computing devices 110 and/or one or more remote computingsystems, such as ISS 160. In addition, UI modules 120 may act asintermediaries between the one or more associated platforms, operatingsystems, applications, and/or services executing at computing devices110, and various output devices of computing devices 110 (e.g.,speakers, LED indicators, audio or haptic output device, etc.) toproduce output (e.g., a graphic, a flash of light, a sound, a hapticresponse, etc.) with computing devices 110. For example, UI module 120Amay cause UIC 112A to output user interface 114 based on data UI module120A receives via network 130 from ISS 160. UI module 120A may receive,as input from ISS 160 information (e.g., audio data, text data, imagedata, etc.) and instructions for presenting as user interface 114.

Query modules 122 perform search related functions for computing devices110. Query modules 122 may transmit search queries (e.g., characterstrings, image data, etc.) via network 130 to ISS 160 and, in response,may obtain results of searches performed by ISS 160 based on thequeries. Query modules 122 may receive indications of the search queriesfrom UI modules 120 as users of computing devices 110 provide inputs atUICs 112. Query modules 122 may output search results received from ISS160 to UI modules 120, for instance, to cause UI modules 120 to presentthe search results as part of user interfaces that UI modules 120present at UICs 112.

After receiving explicit consent from users of computing devices 110 tostore and make use of personal information, context module 162 isconfigured to gather contextual information related to computing devices110 and provides the information to, and makes determinations based onthe contextual information for, modules 164 and 166. As one example,context module 162 may define one or more contexts of computing devices110 and/or a user of computing devices 110.

Context module 162 may encrypt or otherwise treat the information beinganalyzed and/or stored to remove the actual identity of the user beforestoring or making use of the personal information. For example, theinformation may be treated by context module 162 so that anypersonally-identifiable information is removed when stored or sentacross network 130. Context module 162 may only analyze informationassociated with computing devices 110 and/or users of computing devices110 if the user affirmatively consents to use or collection of suchinformation. Context module 162 may further provide opportunities forusers to withdraw consent and in which case, context module 162 maycease collecting or otherwise retaining the information associated withrespective computing devices 110 or the respective users of computingdevices 110.

As used throughout the disclosure, the term “contextual information” isused to describe any conceivable information that may be used by acomputing system and/or computing device, such as ISS 160 and computingdevices 110, to determine one or more environmental or behavioralcharacteristics associated with computing devices and/or users ofcomputing devices. Location and movement information is only some of thetypes of contextual information context module 162 may maintain for eachof computing devices 110. In addition, contextual information mayinclude user topics of interest (e.g., a user's favorite “things”typically maintained as a user interest graph or some other type of datastructure), contact information associated with users (e.g., a user'spersonal contact information as well as information about a user'sfriends, co-workers, social media connections, family, etc.), searchhistories, location histories, long and short term tasks, calendarinformation, application use histories, purchase histories, favorites,bookmarks, and other information that computing devices 110 and ISS 160can gather about a user of computing devices 110.

Furthermore, contextual information may include information about theoperating state of a computing device. For example, an application thatis executed at a given time or in a particular location is an example ofinformation about the operating state of a computing device. Otherexamples of contextual information based on the operating state of acomputing device include, but are not limited to, positions of switches,battery levels, whether a device is plugged into a wall outlet orotherwise operably coupled to another device and/or machine, userauthentication information (e.g., which user is currentlyauthenticated-on or is the current user of the device), whether a deviceis operating in “airplane” mode, in standby mode, in full-power mode,the operational state of radios, communication units, input devices andoutput devices, etc.

A “context” of a computing device may specify one or morecharacteristics associated with the computing device and/or the user ofthe computing device. The context may specify characteristics associatedwith the physical and/or virtual environment of the user and/or thecomputing device at various locations and times. As some examples, acontext of a computing device may specify: an acoustic fingerprint, avideo fingerprint, a location, a movement trajectory, a direction, aspeed, a name of an establishment, a street address, a type of place, abuilding, weather conditions, and traffic conditions, at variouslocations and times. The context of a computing device may specify acalendar event, a meeting, or other event associated with a locationand/or time. The context of a computing device may specify a webpageaddresses viewed at a particular time, one or more text entries made indata fields of the webpages at particular times including search orbrowsing histories, product purchases made at particular times, productwish lists, product registries, and other application usage dataassociated with various locations and times. The context of thecomputing device may further specify audio and/or video accessed by orbeing broadcast in the presence of the computing device at variouslocations and times, television or cable/satellite broadcasts accessedby or being broadcast in the presence the computing device at variouslocations and times, and information about other services accessed bythe computing device at various locations and times.

Context module 162 may update a context of a computing device based onupdated contextual information. For example, context module 162 maydetermine an initial context of computing device 110A or a user ofcomputing device 110A based on initial contextual information associatedwith computing device 110A or the user of computing device 110A. As thecontextual information changes (e.g., based on sensor informationindicative of movement over time), context module 162 may update ordetermine a new context for computing device 110A. For example, contextmodule 162 may obtain online purchase information associated withcomputing device 110A and determine, based on the purchase information,that part of a current context of computing device 110A indicates that auser of computing device 110A recently purchased tickets to a particularmovie or intends to go see the particular movie. After the user goes tothe movie, context module 162 may obtain location history informationassociated with computing device 110A that indicates the user ofcomputing device 110A was at the location of the movie showing indicatedby the previously purchased ticket, at the time it was to be shown.Based on the location information, context module 162 may determine thatpart of the current context of computing device 110A indicates that theuser of computing device 110A went to the particular movie or hasalready seen the particular movie.

Context module 162 may provide the contextual information and makedeterminations about the contextual information that context module 162maintains, on behalf of other modules 164 and 166, as well as computingdevices 110. For example, context module 162 may respond to a requestfrom prediction module 164 of ISS 160 for a current context of computingdevice 110A by outputting, for transmission to prediction module 164,data maintained by context module 162 that specifies the current contextof computing device 110A.

Contextual module 162 may maintain contextual histories associated witheach of computing devices 110 and determine whether a respective,current context associated with one or more of computing devices 110matches a previous context found in a respective contextual history. Forexample, context module 162 may maintain, as part of a contextualhistory associated with computing device 110A, a location history thattracks where computing device 110A was located at a particular day ortime.

Search module 166 may conduct a search (e.g., an internet search) forinformation (e.g., weather or traffic conditions, news, stock prices,sports scores, user schedules, transportation schedules, retail prices,etc.) related to a search query from amongst a variety of informationsources (e.g., either stored locally or remote to ISS 160). Afterexecuting a search, search module 166 may output the informationreturned from the search (e.g., search results, links to search results,etc.) to a component of ISS 160 (e.g., prediction module 164) orcomputing devices 110. Search module 166 may execute a search forinformation determined to be relevant to a search query generated byquery modules 122 and may supplement or modify the search query beingsearched based on one or more contexts determined by context module 162.

Search module 166 may maintain one or more search histories of users ofcomputing devices 110. The search histories maintained by search modules166 may be part of, or separate from, the contextual historiesmaintained by context module 162. The search histories generated bysearch modules 166 may be sortable, and searchable, such that at a giventime, search module 166 may analyze the search histories of one or moreof computing devices 110 to determine what types of queries that usersof computing devices 110 have been searching for. Search module 166 mayprovide module 162 and 164 with access to the search histories and/ormay analyze the search histories and output information from the searchhistories on behalf of modules 162 and 164.

Prediction module 164 is configured to learn and predict an intent of asearch using a search query for a particular context of a computingdevice. Prediction module 164 may be trained based on user-initiatedactions (e.g., inferred from log data) performed by other computingdevices in a variety of contexts. In other words, prediction module 164is configured to learn and predict a purpose for a search using aparticular search query given a particular context and given pastsearches or past actions executed by other computing devices in theparticular context, as well as other contexts.

Prediction module 164 may determine the intent of a search query bydefining a narrow context of a computing device (e.g., indicating a fewspecific characteristics of a computing device/user) and which is morelikely to be related to a search query than, say, a broad context (e.g.,indicating a many specific characteristics of a computing device/user)received from context module 162. That is a broad context of a computingdevice may include a lot of information, however, for determining anintent of a search query, prediction module 164 may only really need toutilize a subset of the information in the broad context. In otherwords, prediction module may filter out all the unnecessary informationfrom a context of a computing device to define a narrow context so atrue intent of the search query can be inferred. Prediction module 164may therefore determine an intent of a search query for a narrowcontext, as opposed to a broad context of a computing device.

Prediction module 164 may execute a machine-learning model (e.g., adeep-learning model) that receives as inputs: a search query (or portionof a search query) and a current context received from context module162. The machine-learning model may generate as output, an indication(such as a label or other identifier) of an intent of a search using thesearch query for the current context.

The intents determined by prediction module 164 may be selected from agroup of predefined intents. Some examples of predefined intents includetransportation or travel related intents (e.g., ride sharing, flightstatus, ticket purchase, schedules, and other transportation relatedintents) and entertainment related intents (e.g., movie review, showtimes, ticket purchase, cast member biography, album or song review,artist biography, artist tour dates, and other entertainment relatedintents).

In some cases, prediction module 164 may generate as output, a scoreassociated with an intent. The score of an intent may indicate a degreeof confidence (e.g., a probability or other degree of likelihood) thatthe purpose of executing a search using the search query in a currentcontext is to obtain information that satisfies the intent. Predictionmodule 164 may adjust, or refrain from adjusting, search resultsdepending on whether the score of the intent satisfies a minimum scoringthreshold. For example, if a score of an intent is less than the minimumscoring threshold (e.g., 50%), prediction module 164 may refrain fromadjusting search results based on an intent and if the score is greaterthan or equal to the minimum scoring threshold, prediction module mayadjust the search results based on the intent.

The machine-learning model of prediction module 164 may be trained onuser interaction data (e.g., log data) indicative of user inputsreceived by computing devices 110 for various contexts. Saiddifferently, the machine-learning model of prediction module 164 may betrained on observable actions executed by computing devices 110 for avariety of contexts. Prediction module 164 may determine which userinputs or user-initiated actions having been observed in similarcontexts, that are also unique to the current context or not necessarilyobserved with relative frequency in other, different contexts. Based onthe user inputs or user-initiated actions that are unique to the currentcontext, prediction module 164 may infer that a user of a computingdevice, in the current context, may perform similar actions. Predictionmodule 164 may determine that an intent of a search using a search queryis to assist the user in performing one or more of the similar actions.

One example of user interaction data (e.g., log data) includesapplication usage data. Application usage data indicates whether anapplication is opened, closed, or installed given a particular context.Prediction module 164 may learn and predict application usage forvarious contexts. For example, prediction module 164 may develop a rulethat on Saturday nights when at home locations, when it is rainingoutside, some users typically interact with a movie review applicationwhereas on Saturday nights when at home locations, when it is notraining outside, some users typically interact with movie ticketpurchasing applications.

One example of user interaction data includes search feature data.Search feature data may indicate which one or more search features wereviewed, clicked, swiped, or otherwise interacted with for a variety ofcontexts. A search feature of a set of search results may be associatedwith curated content. For example, the search feature of a search usinga movie name may be a show time listing, a link to a trailer, moviereview, or other curated content displayed within search results (asopposed to content that a user has to click-through or otherwisenavigate to). For example, prediction module 164 may develop a rule thatfor a particular context, a movie review was shown or clicked on, movieshow times were shown or interacted with, etc. Prediction module 164 maylearns to predict which search features a user may want to click orperform a long view on for a given context.

Prediction module 164 may reference the machine-learning model to definea particular context for a computing device that is most relevant to asearch query at a particular time. For example, prediction module 164may refine the context received from context module 162 to isolate aspecific subset of the one or more device or user characteristicsdefined by the context that are most relevant for inferring an intent ofa search using a search query, at a particular time. For example, withregards to a search query that is a title of a movie, prediction module164 may refine a context of computing device 110A that indicates anynumber of device or user characteristics to instead define a simplecontext of fewer device or user characteristics that indicate it is aSaturday night, computing device 110A is at a home location, while theweather is raining outside.

Given the refined context, and the search query, prediction module 164may determine an intent of a search using the search query. Predictionmodule 164 may determine that the intent is to obtain information toallow a user to perform one or more of the observable actions correlatedwith the context. For example, with a search query about a movie and acontext that indicates it is a Saturday night, computing device 110A isat a home location, and the weather is raining outside, predictionmodule 164 may determine that the intent of the search is to find moviereviews or other information about the movie that excludes purchasingtickets to go see the movie.

Prediction module 164 may request search module 166 perform a searchusing a search query. Prediction module 164 may obtain search resultsfrom search module 166 returned from the requested search performedbased on the search query.

Before sending search results on to computing devices 110, predictionmodule 164 may adjust (e.g., format, rearrange, re-rank, emphasize, oradjust in another way) the search results according to the intent.Prediction module 164 may adjust search results, based on a predictedintent, so as to automatically emphasize information contained in searchresults that a user of a computing device and/or users of othercomputing devices, for similar intents, have needed, in order tocomplete a task or perform an action.

For example, given previous actions (e.g., searches of certain queries)performed by a user of a computing device and/or other users of othercomputing devices, prediction module 164 may infer a task or an actionthe user may need to perform for a particular context. The inferred taskor action may be equivalent to an intent. By inferring the task oraction, prediction module 164 may determine the type of information thata user may be searching for when executing a search using a particularquery.

For example, prediction module 164 may rearrange a ranking of searchresults, including curated and non-curated content, such that reviews ofa movie are ranked higher than ticket purchasing features when thecontext indicates it is a Saturday night, computing device 110A is at ahome location, and it is raining outside. Computing device 110A mayreceive an indication of the adjusted search results and UI module 120Amay cause UIC 112A to present the adjusted search results as part ofuser interface 114.

In this way, when the adjusted search results are output via a userinterface, information that satisfies the intent is output in such a waythat a user is able to quickly identify the information that satisfiesthe intent, from the rest of the search results. In other words, byoutputting adjusted search results, prediction module 164 may emphasizethe portion of search results that a user is more likely to be searchingfor at a current time over other search results.

FIG. 2 is a block diagram illustrating an example computing systemconfigured to predict an intent associated with a search query andadjust search results based on the intent, in accordance with one ormore aspects of the present disclosure. FIG. 2 includes informationserver system (ISS) 260 as an example of ISS 160 of FIG. 1. ISS 260 isdescribed below within the context of system 100 of FIG. 1. FIG. 2illustrates only one particular example of ISS 260, and many otherexamples of ISS 260 may be used in other instances and may include asubset of the components included in example ISS 260 or may includeadditional components not shown in FIG. 2.

ISS 260 provides computing devices 110 with a conduit through which acomputing device, such as computing devices 110A, may execute searchesfor information related to search queries and in some examples,automatically receive adjusted search results that emphasizesinformation that ISS 260 predicts will satisfy the intent of a search orthe needs of users of computing devices 110, for a particular context.As shown in the example of FIG. 2, ISS 260 includes one or moreprocessors 278, one or more communication units 272, and one or morestorage devices 274. Storage devices 274 of ISS 260 include contextmodule 262, prediction module 264, and search module 266. Withinprediction module 264, storage devices 74 includes training module 268.Modules 262-266 include at least the same, if not more, capability as,respectively, modules 162-166 of FIG. 1.

Storage devices 274 of ISS 260 further include logs data store 270A,context history data store 270B, and intent rules data store 270C(collectively, “data stores 270”). Communication channels 276 mayinterconnect each of the components 270, 272, 278, and 274 forinter-component communications (physically, communicatively, and/oroperatively). In some examples, communication channels 276 may include asystem bus, a network connection, an inter-process communication datastructure, or any other method for communicating data.

One or more communication units 272 of ISS 260 may communicate withexternal computing devices, such as computing devices 110 of FIG. 1, bytransmitting and/or receiving network signals on one or more networks,such as network 130 of FIG. 1. For example, ISS 260 may usecommunication unit 272 to transmit and/or receive radio signals acrossnetwork 130 to exchange information with computing devices 110. Examplesof communication unit 272 include a network interface card (e.g. such asan Ethernet card), an optical transceiver, a radio frequencytransceiver, a GPS receiver, or any other type of device that can sendand/or receive information. Other examples of communication units 272may include short wave radios, cellular data radios, wireless Ethernetnetwork radios, as well as universal serial bus (USB) controllers.

One or more storage devices 274 within ISS 260 may store information forprocessing during operation of ISS 260 (e.g., ISS 206 may store dataaccessed by modules 262, 264, 266, and 268 during execution at ISS 260).In some examples, storage devices 274 are a temporary memory, meaningthat a primary purpose of storage devices 274 is not long-term storage.Storage devices 274 on ISS 260 may be configured for short-term storageof information as volatile memory and therefore not retain storedcontents if powered off. Examples of volatile memories include randomaccess memories (RAM), dynamic random access memories (DRAM), staticrandom access memories (SRAM), and other forms of volatile memoriesknown in the art.

Storage devices 274, in some examples, also include one or morecomputer-readable storage media. Storage devices 274 may be configuredto store larger amounts of information than volatile memory. Storagedevices 274 may further be configured for long-term storage ofinformation as non-volatile memory space and retain information afterpower on/off cycles. Examples of non-volatile memories include magnetichard discs, optical discs, floppy discs, flash memories, or forms ofelectrically programmable memories (EPROM) or electrically erasable andprogrammable (EEPROM) memories. Storage devices 274 may store programinstructions and/or data associated with modules 262, 264, 266, and 268.

One or more processors 278 may implement functionality and/or executeinstructions within ISS 260. For example, processors 278 on ISS 260 mayreceive and execute instructions stored by storage devices 274 thatexecute the functionality of modules 262, 264, 266, and 268. Theseinstructions executed by processors 278 may cause ISS 260 to storeinformation, within storage devices 274 during program execution.Processors 278 may execute instructions of modules 262, 264, 266, and268 to learn and predict an intent of a search using a search query orthe informational needs of users of computing devices, for variouscontexts, given the previous actions of other users of other computingdevices, for the same contexts, and automatically provide informationthat is relevant to the predicted needs of a user of a computing device,for a particular context. That is, modules 262, 264, 266, and 268 may beoperable by processors 278 to perform various actions or functions ofISS 270 which are described herein.

Data stores 270 represent any suitable storage medium for storinginformation related to application usage logs, search feature logs, andrules (e.g., of a machine learning system) for predicting the needs ofusers of computing devices or the intent of searches for variouscontexts. The information stored at data stores 270 may be searchableand/or categorized such that one or more modules 262-268 may provide aninput requesting information from one or more of data stores 270 and inresponse to the input, receive information stored at data stores 270.

Before ISS 260 can collect or may make use of information associatedwith a user being stored at any of data stores 270, the user may beprovided with an opportunity to provide input to control whetherprograms or features of ISS 260 can collect and make use of userinformation, or to dictate whether and/or how ISS 260 may receivecontent that may be relevant to the user. In addition, certain datastored by ISS 260 may be treated in one or more ways before it is storedor used by ISS 260, so that personally-identifiable information isremoved. Thus, the user may have control over how information iscollected about the user and used by ISS 260.

Logs data store 270A may be primarily maintained by search module 266and context module 262. Log data store 270A may be part of, or separatefrom, context history data store 270B which is generally maintained bycontext module 262. Log data store 270A may include one or moresearchable data bases or data structures that organize the differenttypes of data logs that are indicative of user inputs received bycomputing devices 110 from which training module 268 may determine rulesfor inferring user-initiated actions performed by computing devices 110,for various contexts. Examples of information contained at logs datastore 270A includes search log data, application (app) log data, andother data (e.g., a query and user interactions on a map application, astreaming video application, an assistant application, etc.).

Search log data includes information that have been searched byindividual computing devices, such as computing devices 110 of FIG. 1 aswell as the information returned from searches, and even userinteraction data (e.g., views, clicks, etc.) associated with theinformation returned from searches. For example, log data store 270A mayinclude rows devoted to individual or groups of computing devices 110and within each row, log data store 270A may include information relatedto searches that search module 266 executed on behalf of the individualor groups computing devices 110. Examples of search log data includesearch terms or queries, times of day and/or locations of the computingdevices that are associated with the search terms, counts associatedwith the search terms that indicate how frequently or how often searchesof the search terms occurred, an indication of whether a search using asearch term was successful (e.g., whether the search result immediatelyin a subsequent search implying that the search was unsuccessful), thesearch features presented, whether a user interacted with such a searchfeature, and any other information related to searches performed bycomputing devices.

App log data includes organized and searchable, historical userinteraction data associated with individual or groups of computingdevices, such as computing devices 110, that is organized according toapplication. The types of application log that may be stored at datastore 270A includes, but is not limited to, application name,application type, duration of application execution, whether anapplication was open, installed, or closed for a particular locationcontext, and any other information that prediction module 264 may needto infer one or more observable actions that are then used to predict anintent of a search using a search query for a particular context.

As indicated above, context history data store 270B may include some orall of log data store 270A. Context history data store 270B includesorganized and searchable, historical contextual information associatedwith individual or groups of computing devices, such as computingdevices 110. They types of contextual information that may be stored atcontext history data store 270B includes, but is not limited to,location information, time of day information, sensor information (e.g.,obtained from computing devices 110), user interest information,information about a devices operating state, application executioninformation (e.g., what and when was the application executed), and anyother information that prediction module 264 may need to predict theneeds of users and infer an intent of a search using a search query.

Intent rules data store 270C includes one or more previously developedrules that prediction module 264 relies on to predict a task or actionlikely to be performed by a user of a computing device for a currentcontext as well as information that the user may need to accomplish thetask. For example, data store 270C may store rules of a machine learningor artificial intelligence system of prediction module 264. The machinelearning or artificial intelligence system of prediction module 264 mayaccess the rules of data store 270C to infer tasks and needs associatedwith users of computing devices 110 for a particular context.

In some examples, prediction module 264 may provide a current context ofa computing device as input to data store 270C and receive as output, anintent or information pertaining to a task or action that a user of thecomputing device may need to perform in the current context and abroader context (that encompasses the current context). In someexamples, the rules of data store 270C may output a degree of likelihood(e.g., a count, a probability, etc.) associated with an intent, task oraction for the current context and a similar degree of likelihoodassociated with the task or action for a broader context. And in someexamples, prediction module 264 may provide the predicted task as inputto data store 270C and receive as output, information pertaining to thetypes of information that the user may need in the current context tocomplete the predicted task.

Training module 268 of prediction module 264 may generate the rulesstored at data store 270C for predicting and determining actions beingperformed by users, and therefore intents, for certain contexts.Training module 268 may generate rules that enable prediction module 264to define a context that is relevant for inferring intent of a searchusing a search query. Training module 268 may generate rules that enableprediction module 264 to determine based on an inferred intent, relevantsearch features to promote or demote when outputting search resultsreturned from a search using a search query in a particular context.

For example, a machine learning or artificial intelligence system oftraining module 268 may analyze the contextual information obtained bycontext module 262 and stored at data store 270B and identifycorrelations between the contextual information and the applicationusage data and the search feature data stored at data store 270A. Typesof machine learning systems used by training module 268 includedeep-learning models, Bayesian networks, neural networks, and othertypes of artificial intelligent models. For example, training module 268may develop a table with a row for each computing device 110 or group ofcomputing device 110 that share similar contexts, similar applicationusage data, and/or similar search feature data associated with thesimilar contexts. Based on the correlations between contextualinformation and user-initiated actions associated with computing devices110, training module 268 may develop rules for defining contexts that,if correlated with particular contexts, may indicate a specific intentof a search being performed by computing devices 110 for that particularcontexts.

Training module 268 may generate rules that enable prediction module 264to determine which one or more characteristics of a context areimportant or related to a search using a search query. Training module268 may use deep learning on the information stored at logs data store270A to predict actions and therefore, intents related to queries andcontexts.

For example, with regards to application usage data, by correlating theapplication usage data with contextual information stored at data store270B, training module 268 may learn in what specific contexts ofcomputing devices 110 that users of computing devices 110 open, close,install, or delete various applications. Depending on the type ofapplication, training module 268 may infer an action being performed oran intent associated with the context. For example, if the applicationis a news or informational application, training module 268 may infer aninformational intent associated with a particular context. If theapplication is a shopping application or a ticket purchasingapplication, training module 268 may infer a commercial intentassociated with a particular context.

With regards to search features, by correlating the application usagedata with contextual information stored at data store 270B, trainingmodule 268 may learn in what specific contexts of computing devices 110that users of computing devices 110 interact, or do not interact, withvarious curated content on a search page. Depending on the type ofsearch feature, training module 268 may infer an action being performedor an intent associated with the context. For example, if the searchfeature is an informational feature (e.g., a movie review), trainingmodule 268 may infer an informational intent associated with aparticular context. If the application is a shopping feature or a ticketpurchasing feature or an event time feature, training module 268 mayinfer a commercial intent associated with a particular context.

Training module 268 may therefore generate more accurate rules at datastore 270C that prediction module 264 uses to predict actualprobabilities/frequencies of actions occurring than other systems. Thatis, training module 268 may generate rules that enable prediction module264 to more easily make comparisons of objective data across variouscontexts and trigger decisions (e.g., determine intents) for variouscontexts, as opposed to performing less accurate predicting based onmore subjective data (e.g., a data set generated by raters about when acertain feature would be useful).

As such, when adjusting search results, prediction module 264 may useother information, besides a user's past searches, location or queryentity type as the only type of contextual information used to determinean intent. Furthermore, adjusted search results that are provided inresponse to a determining a user's specific contextual intent may bedifferent than results and a ranking thereof, returned by search module266, without prediction module 264 expressly determining that intent.

FIG. 3 is a flowchart illustrating example operations performed by anexample computing system configured to predict an intent associated witha search query and adjust search results based on the intent, inaccordance with one or more aspects of the present disclosure. FIG. 3 isdescribed below in the context of system 100 of FIG. 1 and ISS 260 ofFIG. 2. For example, ISS 160 and/or ISS 260 may perform operations310-326 for predicting an intent associated with a search query andadjusting search results based on the intent, in accordance with one ormore aspects of the present disclosure. Operations 310-326 may beperformed in a different order than that shown in FIG. 3. ISS 160 or 260may perform the process shown in FIG. 3 using additional or feweroperations than those shown.

As shown in FIG. 3, in operation, ISS 260 may receive log dataindicative of user inputs received by a group of computing devices forvarious contexts (310). For example, users of computing devices 110 maycause respective query modules 122 to send search queries to ISS 260 toexecute searches for information. In addition to executing searches ofthe search queries, search module 266 may store the search queries,and/or results at data store 270A, to generate one or more searchhistories associated with each of computing devices 110. The searchhistories may include information about various search features viewed,selected, or ignored when search results were presented via UICs 112 tousers of computing devices 110. In addition to executing searches, theusers of computing devices 110 may provide inputs to UICs 112 that causecomputing devices 110 to execute other types of user-initiated actions(e.g., open applications, close applications, install applications,etc.). Context module 262 may store information about the otheruser-initiated actions at data store 270A.

ISS 260 may receive contextual information associated with the group ofcomputing devices (312). For example, context module 262 may storeinformation about a current context associated with each of computingdevices 110, when each computing device 110 performs a user-initiatedaction. The context may indicate a location, a time, an orientation, aspeed, or any other contextual characteristic that may define a context.

ISS 260 may train a model to predict user-initiated actions based on thelog data and the contextual information (314). For example, trainingmodule 268 of prediction module 264 may generate rules that enable adeep-learning model to infer user-initiated actions, and thereforeintent, for various contexts. Using the contextual information, logdata, and other information obtained about computing devices 110, therules may accurately predict, for a particular context, the mostrelevant part of the context with regards to a user's search intent. Forexample, by identifying specific user-initiated actions being performedin specific contexts, that are not also being performed in othercontexts, training module 268 may develop a rule that predicts thespecific user-initiated action to be performed when a future context ofa computing device matches the specific context in which the specificuser-initiated actions are being performed.

ISS 260 may receive a search query from a computing device (316). Forexample, a user of computing device 110A may take a plane to Greece.Upon arrival at an airport in Greece, the user may provide gesture orvoice input to UID 112A that represents a search query being input toquery module 122A. UI module 120A may send information about the searchquery to query module 122A which may then forward the query on to searchmodule 266 of ISS 260 for further processing. The search query mayinclude a single character string for the word “Greek”.

ISS 260 may define a context of the computing device that is relevant tothe search query (318). For example, while contextual module 262 maydetermine a detailed and broad-scoped context of computing device 110Awhile the user is inputting “Greek” into UIC 112A as a search query,prediction module 264 may determine a refined context in order todetermine more accurate intent of the search query. The model ofprediction module 264 may determine unique characteristic defined by thecontext of computing device 110 that are not common across a large groupof device contexts. The model of prediction module 264 may determineuser-initiated actions performed by other computing devices 110, whenthe contexts of the other computing devices 110, shared similar uniquecharacteristics.

For example when at the Greek airport, the context of computing device110A may indicate a popular social media application is executing atcomputing device 110A at the current time, the day of the week isTuesday, the weather is Sunny, the location of computing device 110A isat the airport in Greece, the user recently purchased air plane tickets,the user recently took a flight, the user is texting a contact named“Mother” to send a message that the user arrived safely, etc. Predictionmodule 264 may refine the context of computing device 110A to highlightunique characteristics which may include an indication that the locationof computing device 110A is at the airport in Greece, the user recentlypurchased air plane tickets, the user recently took a flight.

For the defined context, ISS 260 may predict, using the model, an intentof a search using the search query for the context (320). For example,prediction module 264 may input the defined context of computing device110A, along with the search query “Greek” into the model and receive asoutput, an indication that the most likely intent of the search for thespecific context is not to find a Greek restaurant as it might be forother contexts, but instead may be to obtain translations of the Greeklanguage.

ISS 260 may receive search results from a search using the search query(322). For example, prediction module 266 may request that search module266 execute a search for the search query “Greek”. In response to therequest, prediction module 264 may receive search results (e.g., anindication of a search result page) that includes one or more searchfeatures or other search characteristics defined by the search results.That is, the search results received from search module 266 may includeportions of curated content (e.g., search features) such as restaurantreviews, restaurant locations, movie reviews, movie locations, weatherforecasts, facts about culture, and other curated content. One suchpiece of curated content may be a translation feature that enables auser to obtain translations from his or her primary language to Greek.

ISS 260 may adjust the search results to emphasize information thatsatisfies the intent (324). For example, the search results obtainedfrom search module 266 (including the search features described above)may arrive at prediction module 264 in a ranked order. The ranking ofthe search results may place more popular search features or informationhigher than other portions of the search results. For example, Greekrestaurant recommendations may be a high-ranking search results whereasthe translation feature may be a low-ranking search result. Beforeoutputting the indication of the search results to computing device110A, prediction module 264 may adjust the ranking of the search resultsso that the translation feature, i.e., the portion of the search resultsthat corresponds to the inferred intent of the search query, is rankedhigher or more prominently than other search results, such as therestaurant recommendations.

ISS 160 may send an indication of the adjusted search results to thecomputing device (326). For example, prediction module 164 may packagethe adjusted search results as content for presentation at UIC 112A atcomputing device 110A and transmit the content as data over network 130to UI module 120A. UI module 120A may configure UIC 112A to present thecontent (e.g., at a display). When presented at UIC 112A, the portion ofthe search results associated with the translation feature may appearmore prominently that other portions of the search results. For example,the translation feature may appear near an upper portion of a searchpage of a graphical user interface, the translation feature may have aspecial color, texture, or font to distinguish the translation featurefrom other search features and from other content, or may otherwise beemphasized in some other way.

Clause 1. A method comprising: determining, by a computing system, basedon user-initiated actions performed by a group of computing devices, anintent of a search using a particular search query received from acomputing device; adjusting, based on the intent, at least a particularportion of search results obtained from the search using the searchquery by emphasizing information that satisfies the intent; and sending,by the computing system, to the computing device, an indication of theadjusted search results.

Clause 2. The method of clause 1, wherein determining the intent of thesearch of the particular search query comprises: defining, based on theuser-initiated actions performed by the group of computing devices, aparticular context of the computing device that is relevant to theparticular search query, wherein the intent of the search of theparticular search query is further determined based on the particularcontext of the computing device.

Clause 3. The method of any one of clauses 1-2, further comprising:receiving, by the computing system, log data indicative of user inputsreceived by the group of computing devices; and determining, based onthe log data, the user-initiated actions performed by the group ofcomputing devices.

Clause 4. The method of clause 3, wherein the log data includesapplication usage data for the group of computing devices for aplurality of different contexts.

Clause 5. The method of any one of clauses 3-4, wherein the log dataincludes search feature data for the group of computing devices for aplurality of different contexts.

Clause 6. The method of any one of clauses 1-5, wherein adjusting the atleast a particular portion of search results obtained from the searchusing the search query by emphasizing information that satisfies theintent comprises increasing a respective ranking of the at least aportion of search results obtained from the search using the searchquery relative to a respective ranking of other portions of the searchresults obtained from the search using the search query.

Clause 7. The method of any one of clauses 1-6, wherein: the searchresults comprise a plurality of search features, each search featurebeing associated with curated content; the at least a portion of searchresults comprises a particular search feature from the plurality ofsearch features; and adjusting the at least a particular portion ofsearch results obtained from the search using the search query byemphasizing information that satisfies the intent comprises:identifying, by the computing system, based on the intent, theparticular search feature; and increasing a respective ranking of theparticular search relative to respective rankings of other searchfeatures from the plurality of search features.

Clause 8. A computing system comprising: at least one processor; and amemory comprising instructions that when executed, cause the at leastone processor of the computing system to: determine, based onuser-initiated actions performed by a group of computing devices, anintent of a search using a particular search query received from acomputing device; adjust, based on the intent, at least a particularportion of search results obtained from the search using the searchquery by emphasizing information that satisfies the intent; and send, bythe computing system, to the computing device, an indication of theadjusted search results.

Clause 9. The computing system of clause 8, wherein the instructions arepart of a machine-learned model configured to determine the intent basedon the user-initiated actions performed by the group of computingdevices.

Clause 10. The computing system of any one of clauses 8-9, wherein themachine-learned model is a deep-learning model trained on log dataindicative of user inputs received by the group of computing devices.

Clause 12. The computing system of clause 10, wherein the log dataincludes at least one of application usage data for the group ofcomputing devices for a plurality of different contexts or searchfeature data for the group of computing devices for the plurality ofdifferent contexts.

Clause 13. The computing system of any one of clauses 8-12, wherein theinstructions, when executed, cause the at least one processor todetermine the intent of the search of the particular search query by atleast: defining, based on user-initiated actions performed by the groupof computing devices, a particular context of the computing device thatis relevant to the particular search query; and determining the intentof the search of the particular search query based further on theparticular context.

Clause 14. The computing system of any one of clauses 8-13, wherein theinstructions, when executed, cause the at least one processor to adjustthe at least a particular portion of search results obtained from thesearch using the search query by emphasizing information that satisfiesthe intent by at least increasing a respective ranking of the at least aportion of search results obtained from the search using the searchquery relative to a respective ranking of other portions of the searchresults obtained from the search using the search query.

Clause 15. The computing system of any one of clauses 8-14, wherein: thesearch results comprise a plurality of search features, each searchfeature being associated with curated content; the at least a portion ofsearch results comprises a particular search feature from the pluralityof search features; and the instructions, when executed, cause the atleast one processor to adjust the at least a particular portion ofsearch results obtained from the search using the search query byemphasizing information that satisfies the intent by at least:identifying, based on the intent, the particular search feature; andincreasing a respective ranking of the particular search relative torespective rankings of other search features from the plurality ofsearch features.

Clause 16. A computer-readable storage medium comprising instructionsthat, when executed, configure one or more processors of a computingsystem to: determine, based on user-initiated actions performed by agroup of computing devices, an intent of a search using a particularsearch query received from a computing device; adjust, based on theintent, at least a particular portion of search results obtained fromthe search using the search query by emphasizing information thatsatisfies the intent; and send, by the computing system, to thecomputing device, an indication of the adjusted search results.

Clause 17. The computer-readable storage medium of clause 16, whereinthe instructions, when executed, further configure the one or moreprocessors of the computing system to: receive log data indicative ofuser inputs received by the group of computing devices; and determine,based on the log data, the user-initiated actions performed by the groupof computing devices.

Clause 18. The computer-readable storage medium of any one of clauses16-17, wherein the instructions, when executed, configure the at leastone processor to determine the intent of the search of the particularsearch query by at least: defining, based on user-initiated actionsperformed by the group of computing devices, a particular context of thecomputing device that is relevant to the particular search query; anddetermining the intent of the search of the particular search querybased further on the particular context.

Clause 19. The computer-readable storage medium of any one of clauses16-18, wherein the instructions, when executed, configure the at leastone processor to adjust the at least a particular portion of searchresults obtained from the search using the search query by emphasizinginformation that satisfies the intent by at least increasing arespective ranking of the at least a portion of search results obtainedfrom the search using the search query relative to a respective rankingof other portions of the search results obtained from the search usingthe search query.

Clause 20. The computer-readable storage medium of any one of clauses16-19, wherein: the search results comprise a plurality of searchfeatures, each search feature being associated with curated content; theat least a portion of search results comprises a particular searchfeature from the plurality of search features; and the instructions,when executed, configure the at least one processor to adjust the atleast a particular portion of search results obtained from the searchusing the search query by emphasizing information that satisfies theintent by at least: identifying, based on the intent, the particularsearch feature; and increasing a respective ranking of the particularsearch relative to respective rankings of other search features from theplurality of search features.

Clause 21. The computing system of clause 8, comprising means forperforming any of the methods of clauses 1-7.

Clause 22. The computer-readable storage medium of clause 15, comprisingfurther instructions that, when executed configure the one or moreprocessors of the computing system of clause 8 to perform any of themethods of clauses 1-8.

In one or more examples, the functions described may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the functions may be stored on or transmitted over, as oneor more instructions or code, a computer-readable medium and executed bya hardware-based processing unit. Computer-readable medium may includecomputer-readable storage media or mediums, which corresponds to atangible medium such as data storage media, or communication mediaincluding any medium that facilitates transfer of a computer programfrom one place to another, e.g., according to a communication protocol.In this manner, computer-readable medium generally may correspond to (1)tangible computer-readable storage media, which is non-transitory or (2)a communication medium such as a signal or carrier wave. Data storagemedia may be any available media that can be accessed by one or morecomputers or one or more processors to retrieve instructions, codeand/or data structures for implementation of the techniques described inthis disclosure. A computer program product may include acomputer-readable medium.

By way of example, and not limitation, such computer-readable storagemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage, or other magnetic storage devices, flashmemory, or any other storage medium that can be used to store desiredprogram code in the form of instructions or data structures and that canbe accessed by a computer. Also, any connection is properly termed acomputer-readable medium. For example, if instructions are transmittedfrom a website, server, or other remote source using a coaxial cable,fiber optic cable, twisted pair, digital subscriber line (DSL), orwireless technologies such as infrared, radio, and microwave, then thecoaxial cable, fiber optic cable, twisted pair, DSL, or wirelesstechnologies such as infrared, radio, and microwave are included in thedefinition of medium. It should be understood, however, thatcomputer-readable storage mediums and media and data storage media donot include connections, carrier waves, signals, or other transientmedia, but are instead directed to non-transient, tangible storagemedia. Disk and disc, as used herein, includes compact disc (CD), laserdisc, optical disc, digital versatile disc (DVD), floppy disk andBlu-ray disc, where disks usually reproduce data magnetically, whilediscs reproduce data optically with lasers. Combinations of the aboveshould also be included within the scope of computer-readable medium.

Instructions may be executed by one or more processors, such as one ormore digital signal processors (DSPs), general purpose microprocessors,application specific integrated circuits (ASICs), field programmablelogic arrays (FPGAs), or other equivalent integrated or discrete logiccircuitry. Accordingly, the term “processor,” as used herein may referto any of the foregoing structure or any other structure suitable forimplementation of the techniques described herein. In addition, in someaspects, the functionality described herein may be provided withindedicated hardware and/or software modules. Also, the techniques couldbe fully implemented in one or more circuits or logic elements.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses, including a wireless handset, an integratedcircuit (IC) or a set of ICs (e.g., a chip set). Various components,modules, or units are described in this disclosure to emphasizefunctional aspects of devices configured to perform the disclosedtechniques, but do not necessarily require realization by differenthardware units. Rather, as described above, various units may becombined in a hardware unit or provided by a collection ofinteroperative hardware units, including one or more processors asdescribed above, in conjunction with suitable software and/or firmware.

Various embodiments have been described. These and other embodiments arewithin the scope of the following claims.

What is claimed is:
 1. A method comprising: receiving, by a computingsystem, a search query from a computing device of a user, the searchquery including one or more terms; obtaining, by the computing system,contextual information associated with the computing device of the user,wherein the contextual information is in addition to the one or moreterms of the search query; selecting, by the computing system, a subsetof the obtained contextual information that indicates a particularcurrent context associated with the computing device, wherein at leastthe subset of the obtained contextual information excludes informationfrom a search history of the user; determining, by the computing system,a task that the user is likely to perform given the particular currentcontext, wherein the task is a predicted task that is specific to theparticular current context associated with the computing device, andwherein determining the task that the user is likely to perform giventhe particular current context comprises: processing the subset of thecontextual information, using a machine learning model that is based onpast user-initiated actions performed by a group of computing devices ofrespective users, to determine the task; receiving, by the computingsystem, search results obtained responsive to the one or more terms ofthe search query; and subsequent to receiving the search resultsobtained responsive to the one or more terms of the search query:adjusting, by the computing system, based on the determined task thatthe user is likely to perform given the particular current context, atleast a particular portion of the search results obtained responsive tothe one or more terms of the search query; and sending, by the computingsystem, to the computing device of the user, an indication of theadjusted search results.
 2. The method of claim 1, wherein selecting thesubset of the obtained contextual information that indicates aparticular current context associated with the computing device furthercomprises: defining, based on the contextual information associated withthe computing device of the user and the past user-initiated actionsperformed by the group of computing devices, the particular currentcontext associated with the computing device, wherein the particularcurrent context associated with the computing device is defined based onrelevance to the search query.
 3. The method of claim 1, furthercomprising: receiving, by the computing system, log data indicative ofuser inputs received by the group of computing devices; and determining,based on the log data, the past user-initiated actions performed by thegroup of computing devices.
 4. The method of claim 3, wherein: thesearch results include a plurality of search features, each searchfeature being associated with curated content; and at least theparticular portion of the search results comprises a particular searchfeature from the plurality of search features.
 5. The method of claim 4,wherein the past user-initiated actions performed by the group ofcomputing devices include user-initiated actions performed by the groupof computing devices based on receiving user inputs directed to theparticular search feature.
 6. The method of claim 5, wherein processingthe subset of the contextual information, using a machine learning modelthat is based on past user-initiated actions performed by a group ofcomputing devices of respective users, to determine the task that theuser is likely to perform given the particular current contextcomprises: identifying, by the computing system and based on theparticular current context associated with the computing device, theparticular search feature prior to receiving the search results obtainedresponsive to the one or more terms of the search query.
 7. The methodof claim 5, wherein adjusting at least the particular portion of thesearch results obtained responsive to the search query furthercomprises: identifying, by the computing system, based on the determinedtask that the user is likely to perform given the particular currentcontext, the particular search feature; and increasing a respectiveranking of the particular search feature relative to respective rankingsof other search features from the plurality of search features.
 8. Themethod of claim 3, wherein the log data includes at least one ofapplication usage data for the group of computing devices for aplurality of different contexts or search feature data for the group ofcomputing devices for the plurality of different contexts.
 9. A systemcomprising: one or more processors; and memory storing instructionsthat, when executed by the one or more processors, cause the one or moreprocessors to: receive a search query from a computing device of a user,the search query including one or more terms; obtain contextualinformation associated with the computing device of the user, whereinthe contextual information is in addition to the one or more terms ofthe search query; select a subset of the obtained contextual informationthat indicates a particular current context associated with thecomputing device, wherein at least the subset of the obtained contextualinformation excludes information from a search history of the user;determine a task that the user is likely to perform given the particularcurrent context, wherein the task is a predicted task that is specificto the particular current context associated with the computing device,and wherein determining the task that the user is likely to performgiven the particular current context comprises: processing the subset ofthe contextual information, using a machine learning model that is basedon past user-initiated actions performed by a group of computing devicesof respective users, to determine the task; receive search resultsobtained responsive to the one or more terms of the search query; andsubsequent to receiving the search results obtained responsive to theone or more terms of the search query: adjust, based on the determinedtask that the user is likely to perform given the particular currentcontext, at least a particular portion of the search results obtainedresponsive to the one or more terms of the search query; and send, tothe computing device of the user, an indication of the adjusted searchresults.
 10. The system of claim 9, wherein: the search results includea plurality of search features, each search feature being associatedwith curated content; and at least the particular portion of the searchresults comprises a particular search feature from the plurality ofsearch features.
 11. The system of claim 10, wherein processing thesubset of the contextual information, using a machine learning modelthat is based on past user-initiated actions performed by a group ofcomputing devices of respective users, to determine the task that theuser is likely to perform given the particular current contextcomprises: identifying, based on the particular current contextassociated with the computing device, the particular search featureprior to receiving the search results obtained responsive to the one ormore terms of the search query.
 12. The system of claim 9, whereindetermining the task that the user is likely to perform given theparticular current context further comprises: processing the searchquery, along with the subset of the contextual information and using themachine learning model, to determine the task.
 13. The system of claim9, wherein promoting at least the particular portion of the searchresults obtained responsive to the one or more terms of the search querybased on the determined task that the user is likely to perform giventhe particular current context comprises: identifying that a firstsearch result and a second search result are each associated with thedetermined task that the user is likely to perform given the particularcurrent context, wherein the first search result and the second searchresult are responsive to the search query; and displaying the firstsearch result and the second search result more prominently than othersearch results, of the search results, based on identifying that thefirst search result and the second search result are each associatedwith the determined task that the user is likely to perform given theparticular current context.
 14. The system of claim 9, wherein thecontextual information includes at least one of: location information ofthe computing device, movement information of the computing device,weather information for a location of the computing device, a time ofday, or a day of week.
 15. At least one non-transitory computer-readablemedium comprising instructions that, in response to execution of theinstructions by one or more processors, cause the one or more processorsto perform the following operations: receiving a search query from acomputing device of a user, the search query including one or moreterms; obtaining contextual information associated with the computingdevice of the user, wherein the contextual information is in addition tothe one or more terms of the search query; selecting a subset of theobtained contextual information that indicates a particular currentcontext associated with the computing device, wherein at least thesubset of the obtained contextual information excludes information froma search history of the user; determining a task that the user is likelyto perform given the particular current context, wherein the task is apredicted task that is specific to the particular current contextassociated with the computing device, and wherein determining the taskthat the user is likely to perform given the particular current contextcomprises: processing the subset of the contextual information, using amachine learning model that is based on past user-initiated actionsperformed by a group of computing devices of respective users, todetermine the task; receiving search results obtained responsive to theone or more terms of the search query; and subsequent to receiving thesearch results obtained responsive to the one or more terms of thesearch query: adjusting, based on the determined task that the user islikely to perform given the particular current context, at least aparticular portion of the search results obtained responsive to the oneor more terms of the search query; and sending, to the computing deviceof the user, an indication of the adjusted search results.
 16. Thenon-transitory computer-readable medium of claim 15, the operationsfurther comprising: receiving log data indicative of user inputsreceived by the group of computing devices; and determining, based onthe log data, the past user-initiated actions performed by the group ofcomputing devices.
 17. The non-transitory computer-readable medium ofclaim 15, wherein: the search results include a plurality of searchfeatures, each search feature being associated with curated content; atleast the particular portion of the search results comprises aparticular search feature from the plurality of search features; and thepast user-initiated actions performed by the group of computing devicesinclude user-initiated actions performed by the group of computingdevices based on receiving user inputs directed to the particular searchfeature.
 18. The non-transitory computer-readable medium of claim 17,wherein processing the subset of the contextual information, using amachine learning model that is based on past user-initiated actionsperformed by a group of computing devices of respective users, todetermine the task that the user is likely to perform given theparticular current context comprises: identifying, based on theparticular current context associated with the computing device, theparticular search feature prior to receiving the search results obtainedresponsive to the one or more terms of the search query.
 19. Thenon-transitory computer-readable medium of claim 18, wherein adjustingat least the particular portion of the search results obtainedresponsive to the search query comprises: increasing a respectiveranking of the particular search feature relative to respective rankingsof other search features from the plurality of search features.
 20. Thenon-transitory computer-readable medium of claim 18, wherein processingthe subset of the contextual information, using a machine learning modelthat is based on past user-initiated actions performed by a group ofcomputing devices of respective users, to determine the task that theuser is likely to perform given the particular current context furthercomprises: selecting a subset of the past user-initiated actions thatare performed by the group of computing devices based on receiving theuser inputs directed to the particular search feature, wherein thesubset is selected based on the selected user-initiated actions directedto the particular search feature occurring within one or more previouscontexts that correspond to the particular current context and notoccurring within one or more different previous contexts that do notcorrespond to the particular current context; and processing theselected subset of the past user-initiated actions, using the machinelearning model that is based on the past user-initiated actionsperformed by the group of computing devices of respective users, todetermine the task that the user is likely to perform given theparticular current context.